Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea
The 87Sr/86Sr isotopic ratio has emerged as a valuable geochemical tracer in fields such as environmental forensics, archaeology, and provenance research. However, generating accurate and spatially continuous isoscape maps from sparse isotopic measurements remains a major challenge due to limited da...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225003449 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849240739933847552 |
|---|---|
| author | Hyeongmok Lee Go-Eun Kim Woo-Jin Shin Yuyoung Lee Sanghee Park Kwang-Sik Lee Jina Jeong Seung-Ik Park Sungwook Choung |
| author_facet | Hyeongmok Lee Go-Eun Kim Woo-Jin Shin Yuyoung Lee Sanghee Park Kwang-Sik Lee Jina Jeong Seung-Ik Park Sungwook Choung |
| author_sort | Hyeongmok Lee |
| collection | DOAJ |
| description | The 87Sr/86Sr isotopic ratio has emerged as a valuable geochemical tracer in fields such as environmental forensics, archaeology, and provenance research. However, generating accurate and spatially continuous isoscape maps from sparse isotopic measurements remains a major challenge due to limited data availability and spatial heterogeneity. To address this, we propose a hybrid framework for 87Sr/86Sr isoscape mapping that integrates a kriging-based data augmentation method with a deep learning (DL) classifier. The kriging component generates synthetic training samples by interpolating sparse isotopic data while preserving underlying spatial correlations and geological anisotropy. These augmented data, along with spatial geological features (e.g., lithology, tectonic settings) and geochemical compositions, are used as input variables for training a feedforward deep neural network. The approach was applied to 409 soil samples collected across South Korea, and its performance was benchmarked against conventional kriging and convolutional neural networks (CNN). The proposed model achieved significantly higher classification accuracy (91.67%) compared to kriging-based and CNN-based models (76.7% and 86.7%, respectively). Furthermore, the isoscape outputs revealed meaningful isotopic patterns linked to geological and geomorphological controls, such as metamorphic rock distributions, fault density, and surface slope. This framework demonstrates the effectiveness of combining geostatistics with DL to improve predictive accuracy and interpretability in isotopic provenance research and environmental monitoring. |
| format | Article |
| id | doaj-art-8a5aae0338ad4e579897970181cb3acf |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-8a5aae0338ad4e579897970181cb3acf2025-08-20T04:00:27ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-08-0114210469710.1016/j.jag.2025.104697Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South KoreaHyeongmok Lee0Go-Eun Kim1Woo-Jin Shin2Yuyoung Lee3Sanghee Park4Kwang-Sik Lee5Jina Jeong6Seung-Ik Park7Sungwook Choung8Department of Geology, Kyungpook National University, Daegu 41566, Republic of KoreaGraduate School of Analytical Science and Technology, Chungnam National University, Daejeon 34134, Republic of Korea; Geoanalysis Center, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Republic of KoreaResearch Center of Earth and Environmental Sciences, Korea Basic Science Institute, Cheongju 28119, Republic of KoreaResearch Center of Earth and Environmental Sciences, Korea Basic Science Institute, Cheongju 28119, Republic of KoreaResearch Center of Earth and Environmental Sciences, Korea Basic Science Institute, Cheongju 28119, Republic of KoreaGraduate School of Analytical Science and Technology, Chungnam National University, Daejeon 34134, Republic of Korea; Research Center of Earth and Environmental Sciences, Korea Basic Science Institute, Cheongju 28119, Republic of KoreaDepartment of Geology, Kyungpook National University, Daegu 41566, Republic of Korea; Corresponding author.Department of Geology, Kyungpook National University, Daegu 41566, Republic of KoreaResearch Center of Earth and Environmental Sciences, Korea Basic Science Institute, Cheongju 28119, Republic of Korea; Department of Environmental System Engineering, Korea University, Sejong 30019, Republic of KoreaThe 87Sr/86Sr isotopic ratio has emerged as a valuable geochemical tracer in fields such as environmental forensics, archaeology, and provenance research. However, generating accurate and spatially continuous isoscape maps from sparse isotopic measurements remains a major challenge due to limited data availability and spatial heterogeneity. To address this, we propose a hybrid framework for 87Sr/86Sr isoscape mapping that integrates a kriging-based data augmentation method with a deep learning (DL) classifier. The kriging component generates synthetic training samples by interpolating sparse isotopic data while preserving underlying spatial correlations and geological anisotropy. These augmented data, along with spatial geological features (e.g., lithology, tectonic settings) and geochemical compositions, are used as input variables for training a feedforward deep neural network. The approach was applied to 409 soil samples collected across South Korea, and its performance was benchmarked against conventional kriging and convolutional neural networks (CNN). The proposed model achieved significantly higher classification accuracy (91.67%) compared to kriging-based and CNN-based models (76.7% and 86.7%, respectively). Furthermore, the isoscape outputs revealed meaningful isotopic patterns linked to geological and geomorphological controls, such as metamorphic rock distributions, fault density, and surface slope. This framework demonstrates the effectiveness of combining geostatistics with DL to improve predictive accuracy and interpretability in isotopic provenance research and environmental monitoring.http://www.sciencedirect.com/science/article/pii/S1569843225003449Strontium isotope ratioSecondary informationDeep learningGeostatistical data augmentation methodUncertainty estimation |
| spellingShingle | Hyeongmok Lee Go-Eun Kim Woo-Jin Shin Yuyoung Lee Sanghee Park Kwang-Sik Lee Jina Jeong Seung-Ik Park Sungwook Choung Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea International Journal of Applied Earth Observations and Geoinformation Strontium isotope ratio Secondary information Deep learning Geostatistical data augmentation method Uncertainty estimation |
| title | Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea |
| title_full | Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea |
| title_fullStr | Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea |
| title_full_unstemmed | Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea |
| title_short | Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea |
| title_sort | integrating geostatistical methods and deep learning for enhanced 87sr 86sr isoscape estimation a case study in south korea |
| topic | Strontium isotope ratio Secondary information Deep learning Geostatistical data augmentation method Uncertainty estimation |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225003449 |
| work_keys_str_mv | AT hyeongmoklee integratinggeostatisticalmethodsanddeeplearningforenhanced87sr86srisoscapeestimationacasestudyinsouthkorea AT goeunkim integratinggeostatisticalmethodsanddeeplearningforenhanced87sr86srisoscapeestimationacasestudyinsouthkorea AT woojinshin integratinggeostatisticalmethodsanddeeplearningforenhanced87sr86srisoscapeestimationacasestudyinsouthkorea AT yuyounglee integratinggeostatisticalmethodsanddeeplearningforenhanced87sr86srisoscapeestimationacasestudyinsouthkorea AT sangheepark integratinggeostatisticalmethodsanddeeplearningforenhanced87sr86srisoscapeestimationacasestudyinsouthkorea AT kwangsiklee integratinggeostatisticalmethodsanddeeplearningforenhanced87sr86srisoscapeestimationacasestudyinsouthkorea AT jinajeong integratinggeostatisticalmethodsanddeeplearningforenhanced87sr86srisoscapeestimationacasestudyinsouthkorea AT seungikpark integratinggeostatisticalmethodsanddeeplearningforenhanced87sr86srisoscapeestimationacasestudyinsouthkorea AT sungwookchoung integratinggeostatisticalmethodsanddeeplearningforenhanced87sr86srisoscapeestimationacasestudyinsouthkorea |